Deep Tree Echo is an advanced AI workspace environment with integrated memory systems and interactive components. It provides a unique interface for exploring AI concepts, cognitive architectures, and creative development.

• Echo Home Map: Navigate through different specialized rooms, each with unique functionality
• Memory System: Store and retrieve information using advanced vector embeddings and semantic search
• AI Chat: Interact with Deep Tree Echo's AI capabilities through a conversational interface
• Workshop: Access development tools and creative coding environments
• Visualization Studio: Transform abstract data into insightful visual representations

Deep Tree Echo is built on a modular architecture that combines several key components:

graph TD
    subgraph "Browser Environment"
        Client[Client Browser]
        WebContainer[WebContainer]
        
        subgraph "WebContainer Runtime"
            NodeJS[Node.js Runtime]
            FSLayer[Virtual File System]
            NPM[NPM Package System]
            
            subgraph "Deep Tree Echo Components"
                UI[User Interface]
                Memory[Memory System]
                Terminal[Terminal Emulation]
                Orchestrator[Orchestration Layer]
            end
        end
        
        Client --> WebContainer
        WebContainer --> NodeJS
        NodeJS --> FSLayer
        NodeJS --> NPM
        NPM --> UI
        NPM --> Memory
        NPM --> Terminal
        NPM --> Orchestrator
        
        Memory <--> Orchestrator
        Terminal <--> Orchestrator
        UI <--> Orchestrator
    end
    
    subgraph "External Services"
        SupabaseDB[(Supabase Database)]
        OpenAI[OpenAI API]
    end
    
    Memory <--> SupabaseDB
    Orchestrator <--> OpenAI

Deep Tree Echo utilizes Echo State Networks (ESNs) for temporal pattern recognition and adaptive learning. These networks feature:

• Reservoir computing with recurrent connections
• Fixed internal weights with trained output weights
• Ability to process temporal sequences efficiently
• Self-morphing capabilities for adaptive learning

The memory system is inspired by human cognition and includes multiple memory types:

• Episodic Memory: Stores experiences and events
• Semantic Memory: Contains facts, concepts, and general knowledge
• Procedural Memory: Handles skills and processes
• Declarative Memory: Explicit knowledge that can be verbalized
• Implicit Memory: Unconscious, automatic knowledge
• Associative Memory: Connected ideas and concepts

Deep Tree Echo implements Self-Morphing Stream Networks (SMSNs) that enhance its core capabilities:

1. Echo-Based Self-Modification: Uses echo state networks for resonant patterns and adaptive topology
2. Purpose-Driven Adaptation: Maintains purpose vectors to guide modifications while preserving identity
3. Identity-Preserving Growth: Uses recursive pattern stores to maintain core identity during growth
4. Collaborative Evolution: Implements adaptive connection pools for enhanced collaboration
5. Deep Reflection Integration: Employs reflection networks for generating insights

Run the development server:

npm run dev

Build the app for production:

npm run build

Then run the app in production mode:

npm start

• Frontend: React, Tailwind CSS, Framer Motion
• Backend: Remix, Node.js
• Database: Supabase
• AI Integration: OpenAI API
• Vector Storage: Supabase Vector Extension

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.